Overview

Dataset statistics

Number of variables23
Number of observations3677
Missing cells6708
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory631.7 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
balcony is highly overall correlated with bathroom and 1 other fieldsHigh correlation
bathroom is highly overall correlated with area and 6 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 4 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 1 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%)Imbalance
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1802 (49.0%) missing valuesMissing
built_up_area has 1985 (54.0%) missing valuesMissing
carpet_area has 1808 (49.2%) missing valuesMissing
area is highly skewed (γ1 = 29.73502972)Skewed
built_up_area is highly skewed (γ1 = 40.73050927)Skewed
carpet_area is highly skewed (γ1 = 24.31384193)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 465 (12.6%) zerosZeros

Reproduction

Analysis started2024-01-30 08:48:54.694944
Analysis finished2024-01-30 08:49:07.571154
Duration12.88 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

property_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.6 KiB
flat
2816 
house
861 

Length

Max length5
Median length4
Mean length4.2341583
Min length4

Characters and Unicode

Total characters15569
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2816
76.6%
house 861
 
23.4%

Length

2024-01-30T14:19:07.644348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:07.732124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2816
76.6%
house 861
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2816
18.1%
l 2816
18.1%
a 2816
18.1%
t 2816
18.1%
h 861
 
5.5%
o 861
 
5.5%
u 861
 
5.5%
s 861
 
5.5%
e 861
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15569
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2816
18.1%
l 2816
18.1%
a 2816
18.1%
t 2816
18.1%
h 861
 
5.5%
o 861
 
5.5%
u 861
 
5.5%
s 861
 
5.5%
e 861
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15569
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2816
18.1%
l 2816
18.1%
a 2816
18.1%
t 2816
18.1%
h 861
 
5.5%
o 861
 
5.5%
u 861
 
5.5%
s 861
 
5.5%
e 861
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2816
18.1%
l 2816
18.1%
a 2816
18.1%
t 2816
18.1%
h 861
 
5.5%
o 861
 
5.5%
u 861
 
5.5%
s 861
 
5.5%
e 861
 
5.5%
Distinct674
Distinct (%)18.3%
Missing1
Missing (%)< 0.1%
Memory size293.9 KiB
2024-01-30T14:19:08.094646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.863711
Min length1

Characters and Unicode

Total characters61991
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)8.3%

Sample

1st rowmaa bhagwati residency
2nd rowapna enclave
3rd rowtulsiani easy in homes
4th rowsmart world orchard
5th rowparkwood westend
ValueCountFrequency (%)
independent 493
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 167
 
1.7%
emaar 155
 
1.6%
global 152
 
1.6%
m3m 152
 
1.6%
signature 149
 
1.5%
heights 134
 
1.4%
Other values (782) 7491
77.5%
2024-01-30T14:19:08.725065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6711
 
10.8%
5998
 
9.7%
a 5854
 
9.4%
r 4168
 
6.7%
n 4166
 
6.7%
i 3832
 
6.2%
t 3720
 
6.0%
s 3469
 
5.6%
l 2938
 
4.7%
o 2753
 
4.4%
Other values (31) 18382
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55451
89.5%
Space Separator 5998
 
9.7%
Decimal Number 524
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6711
12.1%
a 5854
 
10.6%
r 4168
 
7.5%
n 4166
 
7.5%
i 3832
 
6.9%
t 3720
 
6.7%
s 3469
 
6.3%
l 2938
 
5.3%
o 2753
 
5.0%
d 2491
 
4.5%
Other values (16) 15349
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.5%
2 81
 
15.5%
1 75
 
14.3%
6 55
 
10.5%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 12
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
5998
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55451
89.5%
Common 6540
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6711
12.1%
a 5854
 
10.6%
r 4168
 
7.5%
n 4166
 
7.5%
i 3832
 
6.9%
t 3720
 
6.7%
s 3469
 
6.3%
l 2938
 
5.3%
o 2753
 
5.0%
d 2491
 
4.5%
Other values (16) 15349
27.7%
Common
ValueCountFrequency (%)
5998
91.7%
3 207
 
3.2%
2 81
 
1.2%
1 75
 
1.1%
6 55
 
0.8%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 30
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6711
 
10.8%
5998
 
9.7%
a 5854
 
9.4%
r 4168
 
6.7%
n 4166
 
6.7%
i 3832
 
6.2%
t 3720
 
6.0%
s 3469
 
5.6%
l 2938
 
4.7%
o 2753
 
4.4%
Other values (31) 18382
29.7%

sector
Text

Distinct107
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size266.5 KiB
2024-01-30T14:19:08.953393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length16
Median length9
Mean length9.2058744
Min length8

Characters and Unicode

Total characters33850
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsector 7
2nd rowsector 3
3rd rowsector 47
4th rowsector 61
5th rowsector 92
ValueCountFrequency (%)
sector 3677
49.8%
47 184
 
2.5%
85 108
 
1.5%
102 107
 
1.4%
2 103
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (98) 2747
37.2%
2024-01-30T14:19:09.265549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 3728
11.0%
3705
10.9%
o 3689
10.9%
r 3689
10.9%
s 3685
10.9%
e 3685
10.9%
t 3677
10.9%
1 1084
 
3.2%
7 849
 
2.5%
0 812
 
2.4%
Other values (12) 5247
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22462
66.4%
Decimal Number 7683
 
22.7%
Space Separator 3705
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 3728
16.6%
o 3689
16.4%
r 3689
16.4%
s 3685
16.4%
e 3685
16.4%
t 3677
16.4%
a 212
 
0.9%
d 75
 
0.3%
p 8
 
< 0.1%
h 8
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1084
14.1%
7 849
11.1%
0 812
10.6%
8 806
10.5%
9 763
9.9%
6 742
9.7%
2 713
9.3%
3 652
8.5%
4 651
8.5%
5 611
8.0%
Space Separator
ValueCountFrequency (%)
3705
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22462
66.4%
Common 11388
33.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 3728
16.6%
o 3689
16.4%
r 3689
16.4%
s 3685
16.4%
e 3685
16.4%
t 3677
16.4%
a 212
 
0.9%
d 75
 
0.3%
p 8
 
< 0.1%
h 8
 
< 0.1%
Common
ValueCountFrequency (%)
3705
32.5%
1 1084
 
9.5%
7 849
 
7.5%
0 812
 
7.1%
8 806
 
7.1%
9 763
 
6.7%
6 742
 
6.5%
2 713
 
6.3%
3 652
 
5.7%
4 651
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 3728
11.0%
3705
10.9%
o 3689
10.9%
r 3689
10.9%
s 3685
10.9%
e 3685
10.9%
t 3677
10.9%
1 1084
 
3.2%
7 849
 
2.5%
0 812
 
2.4%
Other values (12) 5247
15.5%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct474
Distinct (%)12.9%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.5324911
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:09.390648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9803552
Coefficient of variation (CV)1.1768472
Kurtosis14.940149
Mean2.5324911
Median Absolute Deviation (MAD)0.72
Skewness3.2802013
Sum9271.45
Variance8.8825169
MonotonicityNot monotonic
2024-01-30T14:19:09.536994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
0.9 63
 
1.7%
1.5 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 58
 
1.6%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (464) 3059
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

HIGH CORRELATION 

Distinct2652
Distinct (%)72.4%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean13889.242
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:09.638154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715
Q16815
median9016
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063

Descriptive statistics

Standard deviation23207.558
Coefficient of variation (CV)1.6709017
Kurtosis186.96493
Mean13889.242
Median Absolute Deviation (MAD)2791
Skewness11.438195
Sum50848515
Variance5.3859076 × 108
MonotonicityNot monotonic
2024-01-30T14:19:09.756477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
6666 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
33333 11
 
0.3%
Other values (2642) 3510
95.5%
(Missing) 16
 
0.4%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1312
Distinct (%)35.8%
Missing16
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2887.4504
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:09.848617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11230
median1733
Q32300
95-th percentile4246
Maximum875000
Range874950
Interquartile range (IQR)1070

Descriptive statistics

Standard deviation23164.366
Coefficient of variation (CV)8.0224291
Kurtosis942.28724
Mean2887.4504
Median Absolute Deviation (MAD)533
Skewness29.73503
Sum10570956
Variance5.3658787 × 108
MonotonicityNot monotonic
2024-01-30T14:19:09.936740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
900 39
 
1.1%
2700 39
 
1.1%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3267
88.8%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2354
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.1 KiB
2024-01-30T14:19:10.327137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.22192
Min length12

Characters and Unicode

Total characters199374
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1847 ?
Unique (%)50.2%

Sample

1st rowCarpet area: 900 (83.61 sq.m.)
2nd rowCarpet area: 650 (60.39 sq.m.)
3rd rowCarpet area: 595 (55.28 sq.m.)
4th rowCarpet area: 1200 (111.48 sq.m.)
5th rowSuper Built up area 1345(124.95 sq.m.)
ValueCountFrequency (%)
area 5572
18.5%
sq.m 3655
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1750
 
5.8%
sq.m.)carpet 1184
 
3.9%
sq.m.)built 702
 
2.3%
plot 683
 
2.3%
carpet 681
 
2.3%
Other values (2844) 8696
28.9%
2024-01-30T14:19:10.835972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26457
 
13.3%
. 20383
 
10.2%
a 13149
 
6.6%
r 9452
 
4.7%
e 9316
 
4.7%
1 9201
 
4.6%
s 7565
 
3.8%
q 7429
 
3.7%
t 7322
 
3.7%
u 6770
 
3.4%
Other values (25) 82330
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82738
41.5%
Decimal Number 47122
23.6%
Space Separator 26457
 
13.3%
Other Punctuation 23397
 
11.7%
Uppercase Letter 8592
 
4.3%
Close Punctuation 5534
 
2.8%
Open Punctuation 5534
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13149
15.9%
r 9452
11.4%
e 9316
11.3%
s 7565
9.1%
q 7429
9.0%
t 7322
8.8%
u 6770
8.2%
p 6764
8.2%
m 5543
6.7%
l 3703
 
4.5%
Other values (5) 5725
6.9%
Decimal Number
ValueCountFrequency (%)
1 9201
19.5%
0 6627
14.1%
2 5687
12.1%
5 4710
10.0%
3 3959
8.4%
4 3711
7.9%
6 3673
 
7.8%
7 3249
 
6.9%
8 3160
 
6.7%
9 3145
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1869
21.8%
U 1145
 
13.3%
P 683
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20383
87.1%
: 3014
 
12.9%
Space Separator
ValueCountFrequency (%)
26457
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5534
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108044
54.2%
Latin 91330
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13149
14.4%
r 9452
10.3%
e 9316
10.2%
s 7565
8.3%
q 7429
8.1%
t 7322
8.0%
u 6770
7.4%
p 6764
7.4%
m 5543
 
6.1%
l 3703
 
4.1%
Other values (10) 14317
15.7%
Common
ValueCountFrequency (%)
26457
24.5%
. 20383
18.9%
1 9201
 
8.5%
0 6627
 
6.1%
2 5687
 
5.3%
) 5534
 
5.1%
( 5534
 
5.1%
5 4710
 
4.4%
3 3959
 
3.7%
4 3711
 
3.4%
Other values (5) 16241
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26457
 
13.3%
. 20383
 
10.2%
a 13149
 
6.6%
r 9452
 
4.7%
e 9316
 
4.7%
1 9201
 
4.6%
s 7565
 
3.8%
q 7429
 
3.7%
t 7322
 
3.7%
u 6770
 
3.4%
Other values (25) 82330
41.3%

bedRoom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3603481
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:10.950285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976489
Coefficient of variation (CV)0.56471796
Kurtosis18.20998
Mean3.3603481
Median Absolute Deviation (MAD)1
Skewness3.4846135
Sum12356
Variance3.6010715
MonotonicityNot monotonic
2024-01-30T14:19:11.051569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1495
40.7%
2 942
25.6%
4 661
18.0%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
12 28
 
0.8%
7 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1495
40.7%
4 661
18.0%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:11.137601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2024-01-30T14:19:11.208330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size238.1 KiB
3+
1171 
3
1073 
2
884 
1
367 
0
182 

Length

Max length2
Median length1
Mean length1.3184661
Min length1

Characters and Unicode

Total characters4848
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3+ 1171
31.8%
3 1073
29.2%
2 884
24.0%
1 367
 
10.0%
0 182
 
4.9%

Length

2024-01-30T14:19:11.310620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:11.412617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2244
61.0%
2 884
 
24.0%
1 367
 
10.0%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2244
46.3%
+ 1171
24.2%
2 884
 
18.2%
1 367
 
7.6%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
75.8%
Math Symbol 1171
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2244
61.0%
2 884
 
24.0%
1 367
 
10.0%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2244
46.3%
+ 1171
24.2%
2 884
 
18.2%
1 367
 
7.6%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2244
46.3%
+ 1171
24.2%
2 884
 
18.2%
1 367
 
7.6%
0 182
 
3.8%

floorNum
Real number (ℝ)

ZEROS 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7936031
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:11.519158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0139108
Coefficient of variation (CV)0.88523141
Kurtosis4.5138569
Mean6.7936031
Median Absolute Deviation (MAD)3
Skewness1.6940936
Sum24851
Variance36.167123
MonotonicityNot monotonic
2024-01-30T14:19:11.609971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 494
13.4%
1 353
 
9.6%
4 315
 
8.6%
8 195
 
5.3%
6 182
 
4.9%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 936
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 353
9.6%
2 494
13.4%
3 498
13.5%
4 315
8.6%
5 169
 
4.6%
6 182
 
4.9%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size233.6 KiB
North-East
623 
East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowWest
3rd rowNorth-East
4th rowSouth-East
5th rowNorth-East

Common Values

ValueCountFrequency (%)
North-East 623
16.9%
East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2024-01-30T14:19:11.923759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:12.084946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
north-east 623
23.7%
east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size280.2 KiB
Relatively New
1647 
New Property
594 
Moderately Old
563 
Undefined
445 
Old Property
304 

Length

Max length18
Median length14
Mean length13.041338
Min length9

Characters and Unicode

Total characters47953
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowOld Property
3rd rowNew Property
4th rowUndefined
5th rowUnder Construction

Common Values

ValueCountFrequency (%)
Relatively New 1647
44.8%
New Property 594
 
16.2%
Moderately Old 563
 
15.3%
Undefined 445
 
12.1%
Old Property 304
 
8.3%
Under Construction 124
 
3.4%

Length

2024-01-30T14:19:12.179017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:12.258259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2241
32.4%
relatively 1647
23.8%
property 898
13.0%
old 867
 
12.5%
moderately 563
 
8.1%
undefined 445
 
6.4%
under 124
 
1.8%
construction 124
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 8573
17.9%
l 4724
 
9.9%
t 3356
 
7.0%
3232
 
6.7%
y 3108
 
6.5%
r 2607
 
5.4%
d 2444
 
5.1%
N 2241
 
4.7%
w 2241
 
4.7%
i 2216
 
4.6%
Other values (15) 13211
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37812
78.9%
Uppercase Letter 6909
 
14.4%
Space Separator 3232
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8573
22.7%
l 4724
12.5%
t 3356
 
8.9%
y 3108
 
8.2%
r 2607
 
6.9%
d 2444
 
6.5%
w 2241
 
5.9%
i 2216
 
5.9%
a 2210
 
5.8%
o 1709
 
4.5%
Other values (7) 4624
12.2%
Uppercase Letter
ValueCountFrequency (%)
N 2241
32.4%
R 1647
23.8%
P 898
13.0%
O 867
 
12.5%
U 569
 
8.2%
M 563
 
8.1%
C 124
 
1.8%
Space Separator
ValueCountFrequency (%)
3232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44721
93.3%
Common 3232
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8573
19.2%
l 4724
10.6%
t 3356
 
7.5%
y 3108
 
6.9%
r 2607
 
5.8%
d 2444
 
5.5%
N 2241
 
5.0%
w 2241
 
5.0%
i 2216
 
5.0%
a 2210
 
4.9%
Other values (14) 11001
24.6%
Common
ValueCountFrequency (%)
3232
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8573
17.9%
l 4724
 
9.9%
t 3356
 
7.0%
3232
 
6.7%
y 3108
 
6.5%
r 2607
 
5.4%
d 2444
 
5.1%
N 2241
 
4.7%
w 2241
 
4.7%
i 2216
 
4.6%
Other values (15) 13211
27.5%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.6483
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:12.373744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.27409
Coefficient of variation (CV)0.39689184
Kurtosis10.338286
Mean1925.6483
Median Absolute Deviation (MAD)372
Skewness1.8343442
Sum3610590.5
Variance584114.89
MonotonicityNot monotonic
2024-01-30T14:19:12.483054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct644
Distinct (%)38.1%
Missing1985
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean2378.188
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:12.599082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.55
Q11100
median1650
Q32400
95-th percentile4689
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17932.315
Coefficient of variation (CV)7.5403272
Kurtosis1669.8364
Mean2378.188
Median Absolute Deviation (MAD)650
Skewness40.730509
Sum4023894
Variance3.2156793 × 108
MonotonicityNot monotonic
2024-01-30T14:19:12.711131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
1350 33
 
0.9%
2700 33
 
0.9%
900 29
 
0.8%
1600 26
 
0.7%
2000 24
 
0.7%
1300 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1388
37.7%
(Missing) 1985
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8067.8 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct732
Distinct (%)39.2%
Missing1808
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean2530.1348
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:12.836296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1845
median1300
Q31790
95-th percentile2938.8
Maximum607936
Range607921
Interquartile range (IQR)945

Descriptive statistics

Standard deviation22818.074
Coefficient of variation (CV)9.0185212
Kurtosis603.56926
Mean2530.1348
Median Absolute Deviation (MAD)470
Skewness24.313842
Sum4728821.9
Variance5.206645 × 108
MonotonicityNot monotonic
2024-01-30T14:19:12.948435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (722) 1575
42.8%
(Missing) 1808
49.2%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2973 
1
704 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2973
80.9%
1 704
 
19.1%

Length

2024-01-30T14:19:13.056175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:13.139803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2973
80.9%
1 704
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 2973
80.9%
1 704
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2973
80.9%
1 704
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2973
80.9%
1 704
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2973
80.9%
1 704
 
19.1%

servant room
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2350 
1
1327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1327
36.1%

Length

2024-01-30T14:19:13.218737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:13.315039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1327
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1327
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2350
63.9%
1 1327
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2350
63.9%
1 1327
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2350
63.9%
1 1327
36.1%

store room
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3339 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Length

2024-01-30T14:19:13.394369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:13.474173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3021 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Length

2024-01-30T14:19:13.540736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:13.619726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3272 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Length

2024-01-30T14:19:13.712559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:13.792073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
1
2436 
2
1038 
0
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2436
66.2%
2 1038
28.2%
0 203
 
5.5%

Length

2024-01-30T14:19:13.856772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-30T14:19:13.931477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2436
66.2%
2 1038
28.2%
0 203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
1 2436
66.2%
2 1038
28.2%
0 203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2436
66.2%
2 1038
28.2%
0 203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2436
66.2%
2 1038
28.2%
0 203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2436
66.2%
2 1038
28.2%
0 203
 
5.5%

luxury_score
Real number (ℝ)

ZEROS 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.441392
Minimum0
Maximum174
Zeros465
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2024-01-30T14:19:14.009179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.092951
Coefficient of variation (CV)0.74316792
Kurtosis-0.88051772
Mean71.441392
Median Absolute Deviation (MAD)38
Skewness0.46011224
Sum262690
Variance2818.8614
MonotonicityNot monotonic
2024-01-30T14:19:14.149486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 465
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2310
62.8%
ValueCountFrequency (%)
0 465
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2024-01-30T14:19:05.869433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:56.482559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.442127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.463576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.648040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.619175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.697802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.666859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.534914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:04.700008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.958212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:56.604987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.531441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.557496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.740834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.710596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.780316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.745493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.630466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:04.788488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.046452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:56.694432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.644925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.643133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.835358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.818209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.861180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.833318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.721059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.034969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.129213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:56.780279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.729447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.777445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.917992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.941500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.089514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.914603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.834436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.158290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.241459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:56.896570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.834966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.870296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.022335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.064760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.177487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.998717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.962721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.249994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.343331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:56.997978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.947925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.141736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.125892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.215622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.261020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.094539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:04.080758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.341199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.452446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.090186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.034356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.225874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.213135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.321728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.343850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.172082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:04.244737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.439755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.542131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.172524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.144080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.307567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.307805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.441710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.423724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.249694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:04.337459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.563596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.662331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.270710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.260071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.402587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.418310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.528330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.504908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.335970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:04.459711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.664313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:06.767870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:57.358714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:58.344354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:18:59.548115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:00.512885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:01.613801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:02.583909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:03.425624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:04.549536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-30T14:19:05.761729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2024-01-30T14:19:14.479428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.000-0.1180.229-0.145-0.160-0.110-0.0690.0900.0640.214-0.0760.1070.184-0.138-0.1100.3540.2820.1410.112-0.082
area-0.1181.0000.0100.6870.6240.8350.8010.0220.1170.0430.2590.0420.0370.7440.2070.0280.0150.0390.0180.948
balcony0.2290.0101.0000.5160.4340.3570.4690.0170.1810.1790.3290.0820.1970.4660.1880.2140.4420.1460.1830.508
bathroom-0.1450.6870.5161.0000.8620.4650.5980.044-0.0040.1950.1790.0700.2860.7200.4110.4710.5190.2440.1760.819
bedRoom-0.1600.6240.4340.8621.0000.3800.5680.032-0.1030.1660.0560.0800.2900.6810.4170.5940.3160.2230.1530.800
built_up_area-0.1100.8350.3570.4650.3801.0000.9691.0000.0930.0900.2900.0000.0000.6050.1330.0000.0000.0000.0000.926
carpet_area-0.0690.8010.4690.5980.5680.9691.0000.0000.1600.0000.2390.0160.0000.6130.1360.0000.0000.0000.0030.894
facing0.0900.0220.0170.0440.0321.0000.0001.0000.0030.0540.0700.0000.029-0.012-0.0280.0950.0360.0360.0000.019
floorNum0.0640.1170.181-0.004-0.1030.0930.1600.0031.0000.0260.2330.0330.1020.002-0.1250.4850.0850.1110.0770.150
furnishing_type0.2140.0430.1790.1950.1660.0900.0000.0540.0261.0000.2300.0640.2130.1860.1260.0840.2660.1560.1370.109
luxury_score-0.0760.2590.3290.1790.0560.2900.2390.0700.2330.2301.0000.1760.1900.2150.0550.3290.3480.2290.1830.220
others0.1070.0420.0820.0700.0800.0000.0160.0000.0330.0640.1761.0000.033-0.001-0.0190.0250.0000.1060.031-0.017
pooja room0.1840.0370.1970.2860.2900.0000.0000.0290.1020.2130.1900.0331.0000.2690.1990.2510.2520.3050.3140.110
price-0.1380.7440.4660.7200.6810.6050.613-0.0120.0020.1860.215-0.0010.2691.0000.7440.5420.3680.3030.2440.773
price_per_sqft-0.1100.2070.1880.4110.4170.1330.136-0.028-0.1250.1260.055-0.0190.1990.7441.0000.2000.0440.0000.0300.287
property_type0.3540.0280.2140.4710.5940.0000.0000.0950.4850.0840.3290.0250.2510.5420.2001.0000.0640.2400.127NaN
servant room0.2820.0150.4420.5190.3160.0000.0000.0360.0850.2660.3480.0000.2520.3680.0440.0641.0000.1610.1840.656
store room0.1410.0390.1460.2440.2230.0000.0000.0360.1110.1560.2290.1060.3050.3030.0000.2400.1611.0000.2260.033
study room0.1120.0180.1830.1760.1530.0000.0030.0000.0770.1370.1830.0310.3140.2440.0300.1270.1840.2261.000-0.015
super_built_up_area-0.0820.9480.5080.8190.8000.9260.8940.0190.1500.1090.220-0.0170.1100.7730.287NaN0.6560.033-0.0151.000

Missing values

2024-01-30T14:19:06.925773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-30T14:19:07.229721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-30T14:19:07.466402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatmaa bhagwati residencysector 70.455000.0900.0Carpet area: 900 (83.61 sq.m.)2214.0WestRelatively NewNaNNaN900.000000128
1flatapna enclavesector 30.507692.0650.0Carpet area: 650 (60.39 sq.m.)2211.0WestOld PropertyNaNNaN650.000000237
2flattulsiani easy in homessector 470.406722.0595.0Carpet area: 595 (55.28 sq.m.)22312.0NaNNew PropertyNaNNaN595.000000136
3flatsmart world orchardsector 611.4712250.01200.0Carpet area: 1200 (111.48 sq.m.)2222.0NaNUndefinedNaNNaN1200.010000176
4flatparkwood westendsector 920.705204.01345.0Super Built up area 1345(124.95 sq.m.)2235.0NaNUnder Construction1345.0NaNNaN1000010
5flatsignature global infinity mallsector 360.416269.0654.0Built Up area: 654 (60.76 sq.m.)2233.0NaNUndefinedNaN654.0NaN0000010
6flatthe cocoonsector 1082.0013333.01500.0Super Built up area 1500(139.35 sq.m.)3335.0NaNNew Property1500.0NaNNaN0000010
7flatats triumphsector 1041.807860.02290.0Carpet area: 2290 (212.75 sq.m.)34314.0NaNNew PropertyNaNNaN2290.000000160
8flatvatika xpressionssector 88b1.108148.01350.0Built Up area: 1350 (125.42 sq.m.)Carpet area: 1050 sq.ft. (97.55 sq.m.)243+2.0North-EastUnder ConstructionNaN1350.01050.010000158
9flatraheja revantasector 784.7516885.02813.0Built Up area: 2813 (261.34 sq.m.)33231.0NaNUndefinedNaN2813.0NaN010001100
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3794houseindependentsector 313.5024155.01449.0Plot area 161(134.62 sq.m.)4332.0South-WestModerately OldNaN1449.0NaN00100281
3795houseindependentsector 465.6523870.02367.0Plot area 263(219.9 sq.m.)863+3.0South-WestModerately OldNaN2367.0NaN01000267
3796houseindependentsector 463.5524500.01449.0Plot area 161(134.62 sq.m.)543+3.0North-WestModerately OldNaN1449.0NaN01000273
3797houseindependentsector 463.6024845.01449.0Plot area 161(134.62 sq.m.)553+3.0South-EastModerately OldNaN1449.0NaN01000275
3798houseindependentsector 553.1020026.01548.0Plot area 172(143.81 sq.m.)543+2.0North-EastModerately OldNaN1548.0NaN01100259
3799houseindependentsector 574.7528787.01650.0Plot area 1600(148.64 sq.m.)Built Up area: 1700 sq.ft. (157.94 sq.m.)Carpet area: 1650 sq.ft. (153.29 sq.m.)3332.0North-WestModerately OldNaN1700.01650.000100296
3800housedlf city phase 1sector 265.5030556.01800.0Plot area 200(167.23 sq.m.)4432.0North-EastModerately OldNaN1800.0NaN11010169
3801housedlf city plots phase 2sector 254.2531481.01350.0Plot area 150(125.42 sq.m.)3232.0NorthOld PropertyNaN1350.0NaN10000135
3802housedlf city phase 1sector 264.5033333.01350.0Plot area 150(125.42 sq.m.)3322.0EastModerately OldNaN1350.0NaN11000170
3803housedlf city phase 1sector 263.2533129.0981.0Plot area 109(91.14 sq.m.)3332.0WestOld PropertyNaN981.0NaN10000179